In [1]:
# Mounting Google Drive
from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive
In [2]:
# Setting the current working directory
import os; os.chdir('/content/drive/My Drive/Python Course')
In [3]:
%tensorflow_version 2.x

Colab only includes TensorFlow 2.x; %tensorflow_version has no effect.
In [4]:
# Imports
import pandas as pd, numpy as np, matplotlib.pyplot as plt, sklearn, re, random
import matplotlib.gridspec as gridspec
from tqdm.notebook import tqdm
import tensorflow, cv2
%matplotlib inline

# Extract content from zipfile
from zipfile import ZipFile

# Model
from tensorflow.keras.layers import ZeroPadding2D, Convolution2D, MaxPooling2D, Dropout, Flatten, Activation
from tensorflow.keras.models import Sequential, Model
from sklearn.svm import SVC

# Encode, standardize and PCA
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
from sklearn.model_selection import RandomizedSearchCV, GridSearchCV
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.decomposition import PCA

# Set random_state
random_state = 2020

# Suppress warnings, if any
import warnings; warnings.filterwarnings('ignore')

# Print versions
print(f'Pandas version: {pd.__version__}')
print(f'Numpy version: {np.__version__}')
print(f'Scikit-learn version: {sklearn.__version__}')
print(f'Tensorflow version: {tensorflow.__version__}')
print(f'CV version: {cv2.__version__}')

Pandas version: 1.5.3
Numpy version: 1.25.2
Scikit-learn version: 1.2.2
Tensorflow version: 2.15.0
CV version: 4.8.0
  1. Unzip, read and Load data(‘PINS.zip’) into session.
In [5]:
with ZipFile('PINS.zip', 'r') as zip:
  zip.extractall()
  1. Write function to create metadata of the image.
  2. Write a loop to iterate through each and every image and create metadata for all the images.
In [6]:
class IdentityMetadata():
    def __init__(self, base, name, file):
        # print(base, name, file)
        # dataset base directory
        self.base = base
        # identity name
        self.name = name
        # image file name
        self.file = file

    def __repr__(self):
        return self.image_path()

    def image_path(self):
        return os.path.join(self.base, self.name, self.file)

def load_metadata(path):
    metadata = []
    exts = []
    for i in os.listdir(path):
        for f in os.listdir(os.path.join(path, i)):
            # Check file extension. Allow only jpg/jpeg' files.
            ext = os.path.splitext(f)[1]
            if ext == '.jpg' or ext == '.jpeg':
                metadata.append(IdentityMetadata(path, i, f))
                exts.append(ext)
    return np.array(metadata), exts

metadata, exts = load_metadata('PINS')
labels = np.array([meta.name for meta in metadata])
In [7]:
def load_image(path):
    img = cv2.imread(path, 1)
    # OpenCV loads images with color channels
    # in BGR order. So we need to reverse them
    return img[...,::-1]
In [104]:
metadata.shape
Out[104]:
(10770,)
In [105]:
metadata
Out[105]:
array([PINS/pins_Aaron Paul/Aaron Paul0_262.jpg,
       PINS/pins_Aaron Paul/Aaron Paul101_247.jpg,
       PINS/pins_Aaron Paul/Aaron Paul101_248.jpg, ...,
       PINS/pins_zendaya/zendaya97.jpg, PINS/pins_zendaya/zendaya98.jpg,
       PINS/pins_zendaya/zendaya99.jpg], dtype=object)
In [14]:
n = np.random.randint(1, len(metadata))
img_path = metadata[n].image_path()
img = load_image(img_path)
In [15]:
fig = plt.figure(figsize = (15, 7.2))
ax = fig.add_subplot(1, 1, 1)
title = labels[n].split('_')[1]
ax.set_title(title, fontsize = 20)
_ = plt.imshow(img)
  1. Generate Embeddings vectors on the each face in the dataset.
In [10]:
def vgg_face():
    model = Sequential()
    model.add(ZeroPadding2D((1, 1), input_shape = (224, 224, 3)))
    model.add(Convolution2D(64, (3, 3), activation = 'relu'))
    model.add(ZeroPadding2D((1, 1)))
    model.add(Convolution2D(64, (3, 3), activation = 'relu'))
    model.add(MaxPooling2D((2, 2), strides = (2, 2)))

    model.add(ZeroPadding2D((1, 1)))
    model.add(Convolution2D(128, (3, 3), activation = 'relu'))
    model.add(ZeroPadding2D((1, 1)))
    model.add(Convolution2D(128, (3, 3), activation = 'relu'))
    model.add(MaxPooling2D((2, 2), strides = (2, 2)))

    model.add(ZeroPadding2D((1, 1)))
    model.add(Convolution2D(256, (3, 3), activation = 'relu'))
    model.add(ZeroPadding2D((1, 1)))
    model.add(Convolution2D(256, (3, 3), activation = 'relu'))
    model.add(ZeroPadding2D((1, 1)))
    model.add(Convolution2D(256, (3, 3), activation = 'relu'))
    model.add(MaxPooling2D((2, 2), strides = (2, 2)))

    model.add(ZeroPadding2D((1, 1)))
    model.add(Convolution2D(512, (3, 3), activation = 'relu'))
    model.add(ZeroPadding2D((1, 1)))
    model.add(Convolution2D(512, (3, 3), activation = 'relu'))
    model.add(ZeroPadding2D((1, 1)))
    model.add(Convolution2D(512, (3, 3), activation = 'relu'))
    model.add(MaxPooling2D((2, 2), strides =(2, 2)))

    model.add(ZeroPadding2D((1, 1)))
    model.add(Convolution2D(512, (3, 3), activation = 'relu'))
    model.add(ZeroPadding2D((1, 1)))
    model.add(Convolution2D(512, (3, 3), activation = 'relu'))
    model.add(ZeroPadding2D((1, 1)))
    model.add(Convolution2D(512, (3, 3), activation = 'relu'))
    model.add(MaxPooling2D((2, 2), strides=(2, 2)))

    model.add(Convolution2D(4096, (7, 7), activation = 'relu'))
    model.add(Dropout(0.5))
    model.add(Convolution2D(4096, (1, 1), activation = 'relu'))
    model.add(Dropout(0.5))
    model.add(Convolution2D(2622, (1, 1)))
    model.add(Flatten())
    model.add(Activation('softmax'))
    return model
In [11]:
model = vgg_face()
model.load_weights('vgg_face_weights.h5')
print(model.summary())

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 zero_padding2d (ZeroPaddin  (None, 226, 226, 3)       0         
 g2D)                                                            
                                                                 
 conv2d (Conv2D)             (None, 224, 224, 64)      1792      
                                                                 
 zero_padding2d_1 (ZeroPadd  (None, 226, 226, 64)      0         
 ing2D)                                                          
                                                                 
 conv2d_1 (Conv2D)           (None, 224, 224, 64)      36928     
                                                                 
 max_pooling2d (MaxPooling2  (None, 112, 112, 64)      0         
 D)                                                              
                                                                 
 zero_padding2d_2 (ZeroPadd  (None, 114, 114, 64)      0         
 ing2D)                                                          
                                                                 
 conv2d_2 (Conv2D)           (None, 112, 112, 128)     73856     
                                                                 
 zero_padding2d_3 (ZeroPadd  (None, 114, 114, 128)     0         
 ing2D)                                                          
                                                                 
 conv2d_3 (Conv2D)           (None, 112, 112, 128)     147584    
                                                                 
 max_pooling2d_1 (MaxPoolin  (None, 56, 56, 128)       0         
 g2D)                                                            
                                                                 
 zero_padding2d_4 (ZeroPadd  (None, 58, 58, 128)       0         
 ing2D)                                                          
                                                                 
 conv2d_4 (Conv2D)           (None, 56, 56, 256)       295168    
                                                                 
 zero_padding2d_5 (ZeroPadd  (None, 58, 58, 256)       0         
 ing2D)                                                          
                                                                 
 conv2d_5 (Conv2D)           (None, 56, 56, 256)       590080    
                                                                 
 zero_padding2d_6 (ZeroPadd  (None, 58, 58, 256)       0         
 ing2D)                                                          
                                                                 
 conv2d_6 (Conv2D)           (None, 56, 56, 256)       590080    
                                                                 
 max_pooling2d_2 (MaxPoolin  (None, 28, 28, 256)       0         
 g2D)                                                            
                                                                 
 zero_padding2d_7 (ZeroPadd  (None, 30, 30, 256)       0         
 ing2D)                                                          
                                                                 
 conv2d_7 (Conv2D)           (None, 28, 28, 512)       1180160   
                                                                 
 zero_padding2d_8 (ZeroPadd  (None, 30, 30, 512)       0         
 ing2D)                                                          
                                                                 
 conv2d_8 (Conv2D)           (None, 28, 28, 512)       2359808   
                                                                 
 zero_padding2d_9 (ZeroPadd  (None, 30, 30, 512)       0         
 ing2D)                                                          
                                                                 
 conv2d_9 (Conv2D)           (None, 28, 28, 512)       2359808   
                                                                 
 max_pooling2d_3 (MaxPoolin  (None, 14, 14, 512)       0         
 g2D)                                                            
                                                                 
 zero_padding2d_10 (ZeroPad  (None, 16, 16, 512)       0         
 ding2D)                                                         
                                                                 
 conv2d_10 (Conv2D)          (None, 14, 14, 512)       2359808   
                                                                 
 zero_padding2d_11 (ZeroPad  (None, 16, 16, 512)       0         
 ding2D)                                                         
                                                                 
 conv2d_11 (Conv2D)          (None, 14, 14, 512)       2359808   
                                                                 
 zero_padding2d_12 (ZeroPad  (None, 16, 16, 512)       0         
 ding2D)                                                         
                                                                 
 conv2d_12 (Conv2D)          (None, 14, 14, 512)       2359808   
                                                                 
 max_pooling2d_4 (MaxPoolin  (None, 7, 7, 512)         0         
 g2D)                                                            
                                                                 
 conv2d_13 (Conv2D)          (None, 1, 1, 4096)        102764544 
                                                                 
 dropout (Dropout)           (None, 1, 1, 4096)        0         
                                                                 
 conv2d_14 (Conv2D)          (None, 1, 1, 4096)        16781312  
                                                                 
 dropout_1 (Dropout)         (None, 1, 1, 4096)        0         
                                                                 
 conv2d_15 (Conv2D)          (None, 1, 1, 2622)        10742334  
                                                                 
 flatten (Flatten)           (None, 2622)              0         
                                                                 
 activation (Activation)     (None, 2622)              0         
                                                                 
=================================================================
Total params: 145002878 (553.14 MB)
Trainable params: 145002878 (553.14 MB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
None
In [12]:
vgg_face_descriptor = Model(inputs = model.layers[0].input, outputs = model.layers[-2].output)
In [13]:
img_path = metadata[0].image_path()
img = load_image(img_path)

# Normalising pixel values from [0-255] to [0-1]: scale RGB values to interval [0, 1]
img = (img / 255.).astype(np.float32)

img = cv2.resize(img, dsize = (224, 224))
print(img.shape)

# Obtain embedding vector for an image
# Get the embedding vector for the above image using vgg_face_descriptor model and print the shape
embedding_vector = vgg_face_descriptor.predict(np.expand_dims(img, axis = 0))[0]
print(embedding_vector.shape)

(224, 224, 3)
1/1 [==============================] - 1s 1s/step
(2622,)
In [34]:
embeddings = []
embeddings = np.zeros((metadata.shape[0], 2622))
for i, meta in tqdm(enumerate(metadata)):
  try:
    image = load_image(str(meta))
    image = (image/255.).astype(np.float32)
    image = cv2.resize(image, (224, 224))
    embeddings[i] = vgg_face_descriptor.predict(np.expand_dims(image, axis = 0))[0]
  except:
    embeddings[i] = np.zeros(2622)

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  1. Build distance metrics for identifying the distance between two similar and dissimilar images
In [35]:
def distance(emb1, emb2):
    return np.sum(np.square(emb1 - emb2))
In [36]:
def show_pair(idx1, idx2):
    plt.figure(figsize = (8, 3))
    plt.suptitle(f'Distance = {distance(embeddings[idx1], embeddings[idx2]):.2f}')
    plt.subplot(121)
    plt.imshow(load_image(metadata[idx1].image_path()))
    plt.subplot(122)
    plt.imshow(load_image(metadata[idx2].image_path()))
In [37]:
show_pair(2, 3)
show_pair(2, 180)
In [38]:
show_pair(30, 31)
show_pair(30, 100)
In [39]:
show_pair(70, 72)
show_pair(70, 115)
  1. Use PCA for dimensionality reduction.
In [40]:
train_idx = np.arange(metadata.shape[0]) % 9 != 0
test_idx = np.arange(metadata.shape[0]) % 9 == 0

# Features
X_train = np.array(embeddings)[train_idx]
X_test = np.array(embeddings)[test_idx]

# Labels
y_train = np.array([meta.name for meta in metadata[train_idx]])
y_test = np.array([meta.name for meta in metadata[test_idx]])

display(X_train.shape, X_test.shape, y_train.shape, y_test.shape)

(9573, 2622)
(1197, 2622)
(9573,)
(1197,)
In [41]:
# Encode labels
en = LabelEncoder()
y_train = en.fit_transform(y_train)
y_test = en.transform(y_test)
In [42]:
# Standarize features
sc = StandardScaler()
X_train_sc = sc.fit_transform(X_train)
X_test_sc = sc.transform(X_test)
In [43]:
# Covariance matrix
cov_matrix = np.cov(X_train_sc.T)

# Eigen values and vector
eig_vals, eig_vecs = np.linalg.eig(cov_matrix)

# Cumulative variance explained
tot = sum(eig_vals)
var_exp = [(i /tot) * 100 for i in sorted(eig_vals, reverse = True)]
cum_var_exp = np.cumsum(var_exp)

print('Cumulative Variance Explained', cum_var_exp)

Cumulative Variance Explained [ 13.57608956  18.94518312  22.92116625 ...  99.99999983  99.99999999
 100.        ]
In [44]:
# Get index where cumulative variance explained is > threshold
thres = 95
res = list(filter(lambda i: i > thres, cum_var_exp))[0]
index = (cum_var_exp.tolist().index(res))
print(f'Index of element just greater than {thres}: {str(index)}')

Index of element just greater than 95: 347
In [45]:
# Ploting
plt.figure(figsize = (15 , 7.2))
plt.bar(range(1, eig_vals.size + 1), var_exp, alpha = 0.5, align = 'center', label = 'Individual explained variance')
plt.step(range(1, eig_vals.size + 1), cum_var_exp, where = 'mid', label = 'Cumulative explained variance')
plt.axhline(y = thres, color = 'r', linestyle = '--')
plt.axvline(x = index, color = 'r', linestyle = '--')
plt.ylabel('Explained Variance Ratio')
plt.xlabel('Principal Components')
plt.legend(loc = 'best')
plt.tight_layout()
In [46]:
# Reducing the dimensions
pca = PCA(n_components = index, random_state = random_state, svd_solver = 'full', whiten = True)
pca.fit(X_train_sc)
X_train_pca = pca.transform(X_train_sc)
X_test_pca = pca.transform(X_test_sc)
display(X_train_pca.shape, X_test_pca.shape)

(9573, 347)
(1197, 347)
  1. Build an SVM classifier in order to map each image to its right person.
In [47]:
svc_pca = SVC(C = 1, gamma = 0.001, kernel = 'rbf', class_weight = 'balanced', random_state = random_state)
svc_pca.fit(X_train_pca, y_train)
print('SVC accuracy for train set: {0:.3f}'.format(svc_pca.score(X_train_pca, y_train)))

SVC accuracy for train set: 0.994
In [48]:
# Predict
y_pred = svc_pca.predict(X_test_pca)

# Accuracy Score
print('Accuracy Score: {}'.format(accuracy_score(y_test, y_pred).round(3)))

Accuracy Score: 0.964
In [50]:
names = [name.split('_')[1].title().strip() for name in labels]

# Classification Report
print('Classification Report: \n{}'.format(classification_report(y_test, y_pred, target_names = np.unique(names))))

Classification Report: 
                          precision    recall  f1-score   support

              Aaron Paul       1.00      1.00      1.00        10
      Alexandra Daddario       0.91      1.00      0.95        10
            Alvaro Morte       1.00      1.00      1.00        14
Alycia Debnam Carey Face       1.00      1.00      1.00        12
             Amanda Crew       1.00      0.71      0.83         7
          Amaury Nolasco       1.00      0.89      0.94         9
        Amber Heard Face       1.00      1.00      1.00         7
               Anna Gunn       0.88      1.00      0.93        14
           Anne Hathaway       1.00      1.00      1.00        14
     Barbara Palvin Face       0.89      0.89      0.89         9
      Bellamy Blake Face       0.87      0.93      0.90        14
    Benedict Cumberbatch       1.00      1.00      1.00        12
            Betsy Brandt       1.00      1.00      1.00        10
              Bill Gates       1.00      0.90      0.95        10
        Brenton Thwaites       0.94      1.00      0.97        15
             Brie Larson       0.94      1.00      0.97        15
            Brit Marling       0.86      1.00      0.92        12
          Bryan Cranston       1.00      1.00      1.00        14
              Caity Lotz       1.00      1.00      1.00        12
        Cameron Monaghan       1.00      1.00      1.00        14
   Chadwick Boseman Face       0.89      1.00      0.94        17
          Chance Perdomo       0.82      1.00      0.90         9
             Chris Evans       1.00      0.93      0.96        14
             Chris Pratt       0.93      1.00      0.97        14
          Cobie Smulders       1.00      0.87      0.93        15
      Danielle Panabaker       1.00      1.00      1.00        14
             Dave Franco       1.00      1.00      1.00        13
            David Mazouz       1.00      1.00      1.00        11
         Dominic Purcell       0.92      1.00      0.96        12
                   Drake       1.00      0.86      0.92         7
           Dua Lipa Face       1.00      1.00      1.00        11
          Dwayne Johnson       1.00      0.92      0.96        12
            Eliza Taylor       1.00      1.00      1.00        13
    Elizabeth Olsen Face       1.00      1.00      1.00        10
               Elon Musk       0.94      1.00      0.97        17
           Emilia Clarke       1.00      0.85      0.92        13
     Emily Bett Rickards       1.00      0.80      0.89         5
              Emma Stone       0.93      1.00      0.97        14
        Emma Watson Face       1.00      1.00      1.00         8
          Gal Gadot Face       1.00      0.79      0.88        14
       Grant Gustin Face       1.00      1.00      1.00        11
         Gwyneth Paltrow       1.00      1.00      1.00        13
             Henry Cavil       1.00      1.00      1.00         8
            Jason Isaacs       1.00      0.93      0.96        14
             Jason Momoa       0.56      1.00      0.72         9
              Jeff Bezos       1.00      1.00      1.00        10
           Jeremy Renner       1.00      1.00      1.00         8
         Jesse Eisenberg       1.00      1.00      1.00         9
             Jim Parsons       1.00      1.00      1.00        10
            Jon Bernthal       0.92      1.00      0.96        11
             Josh Radnor       1.00      1.00      1.00        11
          Kiernan Shipka       1.00      0.93      0.96        14
           Kit Harington       1.00      0.94      0.97        16
    Kristen Stewart Face       0.92      1.00      0.96        12
          Krysten Ritter       0.93      1.00      0.96        13
         Kumail Nanjiani       1.00      1.00      1.00        11
     Lindsey Morgan Face       1.00      1.00      1.00        10
         Maisie Williams       1.00      0.89      0.94         9
      Margot Robbie Face       0.86      1.00      0.92        12
           Maria Pedraza       1.00      1.00      1.00         9
            Mark Ruffalo       1.00      1.00      1.00        12
         Mark Zuckerberg       0.94      0.94      0.94        17
            Martin Starr       1.00      0.93      0.97        15
          Melissa Benoit       1.00      0.89      0.94        18
           Miguel Herran       1.00      1.00      1.00        16
             Mike Colter       1.00      0.94      0.97        17
      Millie Bobby Brown       1.00      1.00      1.00        17
         Morena Baccarin       0.94      0.94      0.94        16
          Morgan Freeman       0.80      0.80      0.80        10
         Natalie Portman       1.00      1.00      1.00        10
     Neil Patrick Harris       1.00      1.00      1.00        14
               Paul Rudd       1.00      1.00      1.00        13
            Pedro Alonso       1.00      1.00      1.00        12
          Peter Dinklage       1.00      0.75      0.86         4
              Rami Melek       1.00      1.00      1.00        15
                 Rihanna       0.89      0.67      0.76        12
                Rj Mitte       1.00      1.00      1.00        20
   Robert Downey Jr Face       1.00      1.00      1.00         9
          Robert Knepper       1.00      0.89      0.94        18
            Robin Taylor       0.85      0.94      0.89        18
           Ryan Reynolds       1.00      1.00      1.00        13
     Sarah Wayne Callies       0.93      1.00      0.97        14
      Scarlett Johansson       1.00      0.90      0.95        10
            Sean Pertwee       1.00      1.00      1.00        18
          Sebastian Stan       0.92      0.92      0.92        13
            Selena Gomez       1.00      1.00      1.00         8
                 Shakira       1.00      1.00      1.00        15
           Sophie Turner       1.00      0.75      0.86         8
           Stephen Amell       1.00      1.00      1.00         7
           Sundar Pichai       1.00      1.00      1.00         9
          Tati Gabrielle       1.00      1.00      1.00         9
            Taylor Swift       1.00      1.00      1.00        13
      Thomas Middleditch       1.00      1.00      1.00        12
            Tom Cavanagh       0.89      0.89      0.89         9
        Tom Holland Face       1.00      1.00      1.00        10
          Ursula Corbero       1.00      1.00      1.00         6
        Wentworth Miller       1.00      1.00      1.00         7
           Willa Holland       0.85      1.00      0.92        11
        William Fichtner       1.00      0.92      0.96        13
                 Zendaya       1.00      1.00      1.00        12

                accuracy                           0.96      1197
               macro avg       0.97      0.96      0.96      1197
            weighted avg       0.97      0.96      0.96      1197
In [107]:
def sample_img_plot(sample_idx):
  # Load image for sample_idx from test data
  sample_img = load_image(metadata[test_idx][sample_idx].image_path())
  # Get actual name
  actual_name = metadata[test_idx][sample_idx].name.split('_')[-1].title().strip()
  # Normalizing pixel values
  sample_img = (sample_img/255.).astype(np.float32)
  # Resize
  sample_img = cv2.resize(sample_img, (224, 224))

  # Obtain embedding vector for sample image
  embedding = vgg_face_descriptor.predict(np.expand_dims(sample_img, axis = 0))[0]
  # Scaled the vector and reshape
  embedding_scaled = sc.transform(embedding.reshape(1, -1))
  # Predict
  sample_pred = svc_pca.predict(pca.transform(embedding_scaled))
  # Transform back
  pred_name = en.inverse_transform(sample_pred)[0].split('_')[-1].title().strip()
  return sample_img, actual_name, pred_name
In [109]:
# Plot for 10th image in test data
sample_img, actual_name, pred_name = sample_img_plot(789)
fig = plt.figure(figsize = (15, 7.2))
plt.axis('off')
plt.imshow(sample_img)
plt.title(f"A: {actual_name} \n P: {pred_name}", color = 'green' if actual_name == pred_name else 'red')
plt.show()

1/1 [==============================] - 1s 1s/step
In [110]:
# Plot for 10th image in test data
sample_img, actual_name, pred_name = sample_img_plot(12)
fig = plt.figure(figsize = (15, 7.2))
plt.axis('off')
plt.imshow(sample_img)
plt.title(f"A: {actual_name} \n P: {pred_name}", color = 'green' if actual_name == pred_name else 'red')
plt.show()

1/1 [==============================] - 1s 905ms/step
  1. Import and display the the test images.
In [90]:
import cv2
import matplotlib.pyplot as plt

# Define the file paths for the test images
image1_path = '/content/drive/My Drive/Python Course/Benedict Cumberbatch9.jpg'
image2_path = '/content/drive/My Drive/Python Course/Dwayne Johnson4.jpg'

# Load the images
image1 = cv2.imread(image1_path)
image2 = cv2.imread(image2_path)

# Convert BGR images to RGB (OpenCV loads images in BGR format by default)
image1 = cv2.cvtColor(image1, cv2.COLOR_BGR2RGB)
image2 = cv2.cvtColor(image2, cv2.COLOR_BGR2RGB)

# Display the images
plt.figure(figsize=(10, 5))

plt.subplot(1, 2, 1)
plt.imshow(image1)
plt.title('Benedict Cumberbatch9.jpg')
plt.axis('off')

plt.subplot(1, 2, 2)
plt.imshow(image2)
plt.title('Dwayne Johnson4.jpg')
plt.axis('off')

plt.show()
  1. Use the trained SVM model to predict the face on both test images
In [93]:
def sample_img_plot(sample_idx):
  # Load image for sample_idx from test data
  sample_img = load_image('/content/drive/My Drive/Python Course/Benedict Cumberbatch9.jpg')
  # Get actual name
  actual_name = metadata[test_idx][sample_idx].name.split('_')[-1].title().strip()
  # Normalizing pixel values
  sample_img = (sample_img/255.).astype(np.float32)
  # Resize
  sample_img = cv2.resize(sample_img, (224, 224))

  # Obtain embedding vector for sample image
  embedding = vgg_face_descriptor.predict(np.expand_dims(sample_img, axis = 0))[0]
  # Scaled the vector and reshape
  embedding_scaled = sc.transform(embedding.reshape(1, -1))
  # Predict
  sample_pred = svc_pca.predict(pca.transform(embedding_scaled))
  # Transform back
  pred_name = en.inverse_transform(sample_pred)[0].split('_')[-1].title().strip()
  return sample_img, actual_name, pred_name
In [99]:
# Plot for 10th image in test data
sample_img, actual_name, pred_name = sample_img_plot(149)
fig = plt.figure(figsize = (15, 7.2))
plt.axis('off')
plt.imshow(sample_img)
plt.title(f"A: {actual_name} \n P: {pred_name}", color = 'green' if actual_name == pred_name else 'red')
plt.show()

1/1 [==============================] - 1s 552ms/step
In [100]:
def sample_img_plot(sample_idx):

  sample_img = load_image('/content/drive/My Drive/Python Course/Dwayne Johnson4.jpg')
  # Get actual name
  actual_name = metadata[test_idx][sample_idx].name.split('_')[-1].title().strip()
  # Normalizing pixel values
  sample_img = (sample_img/255.).astype(np.float32)
  # Resize
  sample_img = cv2.resize(sample_img, (224, 224))

  # Obtain embedding vector for sample image
  embedding = vgg_face_descriptor.predict(np.expand_dims(sample_img, axis = 0))[0]
  # Scaled the vector and reshape
  embedding_scaled = sc.transform(embedding.reshape(1, -1))
  # Predict
  sample_pred = svc_pca.predict(pca.transform(embedding_scaled))
  # Transform back
  pred_name = en.inverse_transform(sample_pred)[0].split('_')[-1].title().strip()
  return sample_img, actual_name, pred_name
In [103]:
sample_img, actual_name, pred_name = sample_img_plot(383)
fig = plt.figure(figsize = (15, 7.2))
plt.axis('off')
plt.imshow(sample_img)
plt.title(f"A: {actual_name} \n P: {pred_name}", color = 'green' if actual_name == pred_name else 'red')
plt.show()

1/1 [==============================] - 1s 846ms/step

Thus from this exercise we were able to conclude the following points:

We were asked to create a face identification model to recognize aligned (faces) for 100 different persons in a dataset containing 10,770 images

Here, we used a pre-trained model (VGG model with pre-trained weights) to generate embeddings for each images in the dataset

Distance between two pair of images were also calculated and plotted Since, there were 2,622 features for each image, PCA was used for dimension reduction after standardizing the features

We used 1200 PCA components where a cummulative explained variance of more than 90% was obtained

In order to predicted the labels for test dataset, Support Vector Machine-Classifier (SVC) was used where an accuracy of 99.4% was obtained The model so obtained was clearly able to predict the 12th image and 789 the image of the dataset

After that we also tried to compare 2 test image bennidict cumberbatch and Dwayne Jhonson it predicted correctly